pg=# \dT+ inboxeventstatus
List of data types
Schema | Name | Internal name | Size | Elements | Owner | Access privileges | Description
--------+------------------+------------------+------+------------+-------+-------------------+-------------
public | inboxeventstatus | inboxeventstatus | 4 | pending +| user | |
| | | | processing+| | |
| | | | completed +| | |
| | | | failed | | |
▐▛███▜▌ Claude Code v2.1.9
▝▜█████▛▘ Opus 4.5 · Claude Max
▘▘ ▝▝ ~/Code/lana-bank-2
❯ tell me, if I were to use an inbox for an event source project + CQRS,
where inbox would be used for commands, what would be the feature of the
inbox. what would be its fields/struct?
⏺ This is a great architectural question. Let me explain the inbox pattern for
event sourcing + CQRS.
Inbox Pattern for Commands
The inbox ensures exactly-once processing of commands through idempotency and
reliable delivery. Here's a typical structure:
struct InboxEntry {
// Identity & Idempotency
id: Uuid, // Primary key
message_id: String, // Idempotency key (dedup commands)
// Command Data
command_type: String, // e.g., "CreateAccount",
"TransferFunds"
payload: serde_json::Value, // Serialized command data
// Processing State
status: InboxStatus, // Pending, Processing, Completed,
Failed
processed_at: Option<DateTime>, // When successfully processed
// Reliability & Retry
received_at: DateTime, // When command arrived
retry_count: u32, // Number of attempts
next_retry_at: Option<DateTime>, // For exponential backoff
error: Option<String>, // Last error message
// Tracing & Context
correlation_id: Option<String>, // Trace across services
causation_id: Option<String>, // What triggered this command
metadata: Option<serde_json::Value>, // Headers, user context, etc.
}
enum InboxStatus {
Pending,
Processing,
Completed,
Failed,
DeadLetter,
}
Key Features
┌────────────────┬─────────────────────────────────────────────────────┐
│ Feature │ Purpose │
├────────────────┼─────────────────────────────────────────────────────┤
│ Idempotency │ message_id ensures duplicate commands are ignored │
├────────────────┼─────────────────────────────────────────────────────┤
│ Atomicity │ Command insert + business logic in same transaction │
├────────────────┼─────────────────────────────────────────────────────┤
│ Reliability │ Retry with backoff on transient failures │
├────────────────┼─────────────────────────────────────────────────────┤
│ Observability │ correlation_id for distributed tracing │
├────────────────┼─────────────────────────────────────────────────────┤
│ Dead-lettering │ Move poison messages after max retries │
└────────────────┴─────────────────────────────────────────────────────┘
Processing Flow
1. Receive command → Insert into inbox (if message_id not exists)
2. Worker picks up Pending entries, marks Processing
3. Execute command handler
4. On success → mark Completed, emit events to outbox
5. On failure → increment retry_count, schedule next_retry_at
Would you like me to explore your codebase to see if there are existing
patterns you're using that we could build upon?
❯ what is the difference between dead-lettering and failed?
⏺ Good question. The distinction is about recoverability:
Status: Failed
Meaning: Transient failure, will retry
What happens next: Worker retries after backoff (e.g., network timeout, DB
lock, temporary unavailability)
────────────────────────────────────────
Status: DeadLetter
Meaning: Permanent failure, won't retry
What happens next: Requires manual intervention (e.g., invalid payload,
business rule violation, max retries exceeded)
Examples
Failed (transient):
- Database connection dropped mid-transaction
- External service returned 503
- Lock contention / timeout
DeadLetter (permanent):
- Command schema validation failed
- Referenced entity doesn't exist and never will
- Retry count exceeded threshold (e.g., 5 attempts)
- Business invariant violated (e.g., insufficient funds with no pending
deposits)
Why separate them?
Pending → Processing → Completed
↓
Failed (retry_count < max)
↓
DeadLetter (retry_count >= max OR unrecoverable)
Dead-lettered messages typically:
1. Trigger alerts for ops/dev teams
2. Get moved to a separate table/queue for analysis
3. May need code fixes before manual replay
This separation keeps your retry loop clean—workers only poll Pending/Failed,
while DeadLetter entries sit untouched until a human investigates.
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